Chenjun Ling

Date of Award

9-2019

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2019

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Engineering

First Advisor

Silvia Figueira

Abstract

With the global expansion of the Internet and the World Wide Web, users are becoming increasingly diverse, particularly in terms of languages. In fact, the number of polyglot Web users across the globe has increased dramatically.

However, even such multilingual users often continue to suffer from unbalanced and fragmented news information, as traditional news access systems seldom allow users to simultaneously search for and/or compare news in different languages, even though prior research results have shown that multilingual users make significant use of each of their languages when searching for information online.

Relatively little human-centered research has been conducted to better understand and support multilingual user abilities and preferences. In particular, in the fields of cross-language and multilingual search, the majority of research has focused primarily on improving retrieval and translation accuracy, while paying comparably less attention to multilingual user interaction aspects.

The research presented in this thesis provides the first large-scale investigations of multilingual news consumption and querying/search result selection behaviors, as well as a detailed comparative analysis of polyglots’ preferences and behaviors with respect to different multilingual news search interfaces on desktop and mobile platforms. Through a set of 4 phases of user studies, including surveys, interviews, as well as task-based user studies using crowdsourcing and laboratory experiments, this thesis presents the first human-centered studies in multilingual news access, aiming to drive the development of personalized multilingual news access systems to better support each individual user.

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